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A Forecasting Comparison of Classical and Bayesian Methods for Modelling Logistic Diffusion

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  • Bewley, Ronald
  • Griffiths, William E

Abstract

A Bayesian procedure for forecasting S-shaped growth is introduced and compared to classical methods of estimation and prediction using three variants of the logistic functional form and annual times series of the diffusion of music compact discs in twelve countries. The Bayesian procedure was found not only to improve forecast accuracy, using the medians of the predictive densities as point forecasts, but also to produce intervals with a width and asymmetry more in accord with the outcomes than intervals from the classical alternative. While the analysis in this paper focuses on logistic growth, the problem is set up so that the methods are transportable to other characterizations of the growth process. Copyright © 2001 by John Wiley & Sons, Ltd.

Suggested Citation

  • Bewley, Ronald & Griffiths, William E, 2001. "A Forecasting Comparison of Classical and Bayesian Methods for Modelling Logistic Diffusion," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 231-247, July.
    • Handle: RePEc:jof:jforec:v:20:y:2001:i:4:p:231-47
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    Cited by:

    1. B Aytac & S D Wu, 2011. "Modelling high-tech product life cycles with short-term demand information: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 425-432, March.
    2. Abualkhair, Ayman, 2007. "Electricity sector in the Palestinian territories: Which priorities for development and peace?," Energy Policy, Elsevier, vol. 35(4), pages 2209-2230, April.
    3. James, Gareth M. & Sood, Ashish, 2006. "Performing hypothesis tests on the shape of functional data," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1774-1792, April.
    4. Bewley, Ronald & Griffiths, William E., 2003. "The penetration of CDs in the sound recording market: issues in specification, model selection and forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 111-121.

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